CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations (2012.02333v3)

Abstract: Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC's power, but today's Noisy Intermediate-Scale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative--purely classical simulations--and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the state-of-the-art large NISQ devices achieve. Overall, this hybrid approach allows users to leverage classical and quantum computing resources to evaluate quantum programs far beyond the reach of either one alone.

• The paper introduces CutQC, a hybrid method that partitions large quantum circuits for independent execution on small NISQ devices.
• It employs a mixed-integer programming model to optimize cut locations and a dynamic query algorithm to effectively sample dense probability distributions.
• Experimental results demonstrate runtime speedups from 60X to 8600X and fidelity improvements between 21% and 47% over conventional methods.

Exploring CutQC: Enhancing Quantum Circuit Evaluations with a Hybrid Approach

The paper "CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations" presents an insightful investigation into a hybrid computational method designed to leverage both classical and quantum computing resources. The authors introduce an innovative approach named CutQC, which is particularly relevant for today's noisy intermediate-scale quantum (NISQ) devices that face substantial scalability and fidelity challenges.

CutQC proposes a strategic method for executing large quantum circuits by partitioning them into smaller subcircuits. These subcircuits can be independently executed on smaller quantum devices, with classical postprocessing techniques reconstructing the entire circuit's outcome. This method effectively allows for the evaluation of quantum circuits beyond the size and noise limitations of individual quantum or classical computational methods. The experimental results indicate a runtime speedup ranging from 60X to 8600X compared to purely classical simulations, demonstrating CutQC's effectiveness in extending the reachable size of quantum circuits. Additionally, when tested on various benchmark circuits, CutQC showed an improvement in execution fidelity by an average of 21% to 47% over larger quantum devices.

The core challenge addressed by this research is the limited capacity of NISQ devices to reliably execute large-scale quantum computations due to their inherent noise levels and restricted qubit counts. Contemporary full-state classical simulations, conversely, are stymied by the exponential memory and computational requirements as the qubit number increases. CutQC navigates these obstacles by strategically combining the two computational realms, maintaining the strengths of quantum computing's exponential state space expansion and classical computing's precision in handling noisy data.

Several key features underpin this hybrid approach. Firstly, a mixed-integer programming (MIP) model is developed to systematically determine optimal cut locations within a quantum circuit. This model effectively minimizes the required classical postprocessing workload, thus optimizing the overall computational efficiency. Furthermore, the authors introduce a dynamic definition (DD) query algorithm to efficiently sample the output of large circuits without needing to store all possible outcomes. This algorithm proves particularly beneficial for quantum circuits generating dense probability distributions, offering an adaptive zoom-in capability on regions of interest.

The implications of CutQC span both practical and theoretical aspects of quantum computing development. Practically, by enabling the execution of larger quantum circuits using existing smaller quantum hardware, CutQC facilitates the application of quantum computing to a broader range of problem domains. On a theoretical level, CutQC contributes to the discourse on hybrid quantum-classical methodologies, potentially guiding future developments toward robust algorithms that seamlessly integrate the capabilities of both computational domains.

Looking toward future advancements, the techniques delineated in this research could form the foundation for scalable quantum computing applications, particularly as fidelity and qubit counts in quantum devices improve. As the community progresses toward error-corrected quantum computing, the principles embodied in CutQC may well inform the hybrid strategies that bridge the current and next stages of quantum technological evolution.

Overall, "CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations" elucidates a strategic hybrid framework with the potential to substantially augment the capability and scalability of quantum computing practices within the current technological landscape.